Modeling and Analysis of Dynamic Computer Experiments
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Bibliographic record
Abstract
Dynamic computer experiments which refer to computer experiments with \ntime series outputs have increasingly gained popularity in both \nscience and engineering. Analysis of dynamic computer experiments \nthrough statistical emulators or surrogate models emerges as an \nimportant topic in statistical literature. This thesis is devoted to \nthree research topics in modeling and analysis of dynamic computer \nexperiments. We propose new methodologies for (a) efficient inference \nof Gaussian process models for large-scale dynamic computer \nexperiments; (b) the inverse problem for small-scale dynamic computer \nexperiments, that is, when a target response is available, we aim to \nestimate the inputs of the computer simulator that produce a response \nmatching the target as closely as possible; (c) the inverse problem in \nlarge-scale dynamic computer experiments, which requires fitting the \nGaussian process emulator efficiently given a large input data set to \nobtain the estimated solution to the inverse problem. \n \nFor the large-scale dynamic computer experiments, we propose a local \napproximate singular value decomposition based Gaussian process \n(lasvdGP) model, which is shown to provide accurate and efficient \nemulation for the dynamic computer simulator. For the small-scale \ninverse problem, we introduce a sequential design approach which \nselects follow-up design points as per a proposed expected improvement \ncriterion. The effectiveness of this approach is verified by both the \ntheoretical study of convergence and the empirical study compared with \nexisting alternative methods. For the inverse problem in large-scale \ndynamic computer experiments, we propose an approximate Bayesian \ninference algorithm using the proposed lasvdGP model. This approach \ngives promising results to address the computational challenge of the \nlarge input data set of the dynamic computer simulator.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it